Rebecca Hwa
ABSTRACT
We present an empirical study of the applicability of Probabilistic Lexicalized Tree Insertion Grammars (PLTIG), a lexicalized counterpart to Probabilistic Context-Free Grammars (PCFG), to problems in stochastic natural-language processing. Comparing the performance of PLTIGs, with non-hierarchical N-gram models and PCFGs, we show that PLTIG combines the best aspects of both, with language modeling capability comparable to N-gram models and PCFGs, we show that PLTIG combines the best aspects of both, with language modeling capability comparable to N-grams, and improved parsing performance over its nonlexicalized counterpart. Furthermore, training of PLTIGs displays faster convergence than PCFGs.
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Lexicalized context-free grammars
ACL '93: Proceedings of the 31st annual meeting on Association for Computational LinguisticsLexicalized context-free grammar(LCFG) is an attractive compromise between the parsing efficiency of context-free grammar (CFG) and the elegance and lexical sensitivity of lexicalized tree adjoining grammar (LTAG). LCFG is a restricted form of LTAG that ...
Tree insertion grammar: a cubic-time, parsable formalism that lexicalizes context-free grammar without changing the trees produced
Tree insertion grammar (TIG) is a tree-based formalism that makes use of tree substitution and tree adjunction. TIG is related to tree adjoining grammar. However, the adjunction permitted in TIG is sufficiently restricted that TIGs only derive context-...
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